PJM440 MOD5 Discussion Post, Peer responses.
Please reply to both POST1: and POST2: in at least 250 words. I have included the original post and references to help out with your responses.
Required
- Chapters 17 & 18 in Quality Management for Organizational Excellence: Introduction to Total Quality
- Snee, R. D., & Hoerl, R. W. (2018). The future of quality. The Journal of Quality and Participation, 40(4), 11-17.
- Slides 188-226 in the PowerPoint supplement to Quality Management for Organizational Excellence: Introduction to Total Quality
Recommended
- Bazrkar, A., Iranzadeh, S., & Feghhi Farahmand, N. (2017). Total quality model for aligning organization strategy, improving performance, and improving customer satisfaction by using an approach based on combination of balanced scorecard and lean six sigma. Cogent Business & Management, 4(1), 1-16.
- Singh, J., Singh, H., Pandher, R. P. S. (2017). Role of DMAIC approach in manufacturing unit: A case study. IUP Journal of Operations Management, 16(4), 52-67.
References
American Society for Quality (ASQ). (2018a). What is quality function deployment (QFD)? Retrieved from http://asq.org/learn-about-quality/qfd-quality-fun…
American Society for Quality (ASQ). (2018b). Seven new management and planning tools. Retrieved from http://asq.org/learn-about-quality/new-management-…
Goetsch, D. L., & Davis, S. D. (2016). Quality management for organizational excellence: Introduction to total quality (8th ed.). Upper Saddle River, NJ: Pearson Education, Inc.
Tague, N. (2004). The quality toolbox (2nd ed.). Milwaukee, WI: ASQ Quality Press.
Original Post:
A manufacturer produces bolts for commercial use, and tests the quality of production by choosing a sample of 5 bolts every 2 hours from production. Because of variation, not all bolts have the same length. Shown below are 8 samples. Use the information below to construct an x bar chart, and R chart, and determine if the process is capable and in control.
- Sample 1: 8.01, 7.99, 7.99. 8.00, 7.98
- Sample 2: 8.03, 7.96, 8.01, 8.02, 7.97
- Sample 3: 7.96, 7.97, 7.96, 7.99, 8.01
- Sample 4: 8.00, 8.01, 7.97, 8.01, 7.99
- Sample 5: 8.02, 8.03, 7.99, 8.01, 8.02
- Sample 6: 8.02, 8.01, 7.97, 8.00, 8.01
- Sample 7: 8.03, 8.04, 8.03, 8.00, 7.99
- Sample 8: 7.96, 7.99, 8.02, 8.01, 7.98
Prepare your charts using any tool you wish, i.e., PowerPoint, MS Word, Excel, or free tools such as Apache OpenOffice and portable and installable versions of LibreOffice. Save your document as a pdf file for uploading to your discussion area. Answer the following questions:
- Is the process capable and in control?
- Why do we need both an x bar and R chart?
- When a process is in control, is it producing acceptable levels of quality?
POST1:
Quality control charts are a tool used to monitor if a process is under statistical control. The charts help to visualize differences and allow for problems to be addressed quickly. X-bar and R-chart are a type of control chart used to measure the variation between samples taken at a given time. The control limits within each chart are used to monitor the difference in mean for the future. For example, if a point is outside of the control limits, the mean or variation of the process is considered out of control (Goetsch & Davis, 2014).
On an X-bar chart, the y-axis depicts the control limits and overall mean, but the x-axis shows the sample group. X-Bar charts provide useful information such as sample size, control limits, standard deviation, overall mean, and the points beyond control. However, on the R-chart, the y-axis depicts the range, overall mean, and control limits (Goetsch & Davis, 2014).
According to the X-Bar chart and R-Chart, all of the samples are in the valid range of control. Both X-Bar and R-Charts are needed because the X-bar chart shows how the average changes over time and the R-chart shows how the range of the samples change over time. When a process is in control, it does not necessarily mean that the process is producing acceptable levels of quality. This means that the process is producing minimal levels of variation. Also, in terms of quality, there is a difference between customer satisfaction and quality.
References
Goetsch, D. L., & Davis, S. (2014). Quality management for organizational excellence : introduction to total quality. Pearson.
POST2:
When charting data that has more than two samples of data, you want to consider using x-charts and R-charts. In the sample data given and graphed (see attachment), here are the results.
- Is the process capable and in control?
By graphing the sample data set, you can see that there is very little variation in the length of the bolts every 2 hours when tested. To prove that the production quality is to standard, the Upper Control Limit and Lower Control Limit (UCL & LCL) are set. UCL is set at 44.63442 and the LCL is a negative number, which essentially means the LCL will be set at zero. When you look at the data within these lines on the graph, you will see that the data is capable, and quality is well within control.
- Why do we need both an x bar and R chart?
An x-chart is used to show the mean (average) of samples over time and an R-chart is used to show the variation, or range, in each sample over time (Goetsch & Davis, 2014).
- When a process is in control, is it producing acceptable levels of quality?
Yes, it is. The UCL and LCL are set to be the guiderails in a graphed process for quality. Any data point going outside of these limits creates instability in a process and is a great indicator to the leaders of a project that there is an issue to address to ensure consistent quality.
Reference:
Goetsch, D. L., & Davis, S. (2014). Quality management for organizational excellence: introduction to total quality. Pearson.
Do you need a similar assignment done for you from scratch? We have qualified writers to help you. We assure you an A+ quality paper that is free from plagiarism. Order now for an Amazing Discount!
Use Discount Code “Newclient” for a 15% Discount!
NB: We do not resell papers. Upon ordering, we do an original paper exclusively for you.
